Abstract
Pest monitoring models play a vital role in enabling informed decisions for pest control and effective management strategies. In the context of smart farming, various approaches have been developed, surpassing traditional techniques in both efficiency and accuracy. However, the application of Few-Shot Learning (FSL) methods in this domain remains limited. In this study, we aim to bridge this gap by leveraging Transfer Learning (TL). Our findings highlight the considerable efficacy of TL techniques in this context, showcasing a significant 24% improvement in mAP performance and a 10% reduction in training time, thereby enhancing the efficiency of the model training process.
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Acknowledgments
This work was supported by project PEGADA 4.0 (PRR-C05-i03-000099), financed by the PPR - Plano de Recuperação e Resiliência and by national funds through FCT, within the scope of the project CISUC (UID/CEC/00326/2020).
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Costa, D., Silva, C., Costa, J., Ribeiro, B. (2024). Improving Pest Detection via Transfer Learning. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14470. Springer, Cham. https://doi.org/10.1007/978-3-031-49249-5_8
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